High-Throughput Mass Spectrometry Imaging with Dynamic Sparse Sampling

Hardware and software solutions for implementing nanospray desorption electrospray ionization (nano-DESI) sources on commercial mass spectrometers

Nanospray desorption electrospray ionization (nano-DESI) is an ambient ionization mass spectrometry imaging (MSI) approach that enables spatial mapping of biological and environmental samples with high spatial resolution and throughput. Because nano-DESI has not yet been commercialized, researchers develop their own sources and interface them with different commercial mass spectrometers. Previously, several protocols focusing on the fabrication of nano-DESI probes have been reported. In this tutorial, we discuss different hardware requirements for coupling the nano-DESI source to commercial mass spectrometers, such as the safety interlock, inlet extension, and contact closure. In addition, we describe the structure of our custom software for controlling the nano-DESI MSI platform and provide detailed instructions for its usage. With this tutorial, interested researchers should be able to implement nano-DESI experiments in their labs.

Gábor Transform-Based Signal Isolation, Rapid Deconvolution, and Quantitation of Intact Protein Ions with Mass Spectrometry

High-resolution mass spectrometry (HRMS) is a powerful technique for the characterization and quantitation of complex biological mixtures, with several applications including clinical monitoring and tissue imaging. However, these medical and pharmaceutical applications are pushing the analytical limits of modern HRMS techniques, requiring either further development in instrumentation or data processing methods. Here, we demonstrate new developments in the interactive Fourier-transform analysis for mass spectrometry (iFAMS) software including the first application of Gábor transform (GT) to protein quantitation. Newly added automation tools detect signals from minimal user input and apply thresholds for signal selection, deconvolution, and baseline correction to improve the objectivity and reproducibility of deconvolution. Additional tools were added to improve the deconvolution of highly complex or congested mass spectra and are demonstrated here for the first time. The "Gábor Slicer" enables the user to explore trends in the Gábor spectrogram with instantaneous ion mass estimates accurate to 10 Da. The charge adjuster allows for easy visual confirmation of accurate charge state assignments and quick adjustment if necessary. Deconvolution refinement utilizes a second GT of isotopically resolved data to remove common deconvolution artifacts. To assess the quality of deconvolution from iFAMS, several comparisons are made to deconvolutions using other algorithms such as UniDec and an implementation of MaxEnt in Agilent MassHunter BioConfirm. Lastly, the newly added batch processing and quantitation capabilities of iFAMS are demonstrated and compared to a common extracted ion chromatogram approach.

Multiscale biochemical mapping of the brain through deep-learning-enhanced high-throughput mass spectrometry

Spatial omics technologies can reveal the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive biochemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping using MEISTER, an integrative experimental and computational mass spectrometry (MS) framework. Our framework integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating three-dimensional (3D) molecular distributions and a data integration method fitting cell-specific mass spectra to 3D datasets. We imaged detailed lipid profiles in tissues with millions of pixels and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future development of multiscale technologies for biochemical characterization of the brain.

Integrative Multiscale Biochemical Mapping of the Brain via Deep-Learning-Enhanced High-Throughput Mass Spectrometry

Elucidating the spatial-biochemical organization of the brain across different scales produces invaluable insight into the molecular intricacy of the brain. While mass spectrometry imaging (MSI) provides spatial localization of compounds, comprehensive chemical profiling at a brain-wide scale in three dimensions by MSI with single-cell resolution has not been achieved. We demonstrate complementary brain-wide and single-cell biochemical mapping via MEISTER, an integrative experimental and computational mass spectrometry framework. MEISTER integrates a deep-learning-based reconstruction that accelerates high-mass-resolving MS by 15-fold, multimodal registration creating 3D molecular distributions, and a data integration method fitting cell-specific mass spectra to 3D data sets. We imaged detailed lipid profiles in tissues with data sets containing millions of pixels, and in large single-cell populations acquired from the rat brain. We identified region-specific lipid contents, and cell-specific localizations of lipids depending on both cell subpopulations and anatomical origins of the cells. Our workflow establishes a blueprint for future developments of multiscale technologies for biochemical characterization of the brain.

High-resolution integrated microfluidic probe for mass spectrometry imaging of biological tissues

Nanospray desorption electrospray ionization (nano-DESI) is an ambient ionization technique that enables molecular imaging of biological samples with high spatial resolution. We have recently developed an integrated microfluidic probe (iMFP) for nano-DESI mass spectrometry imaging (MSI) that significantly enhances the robustness of the technique. In this study, we designed a new probe that enables imaging of biological samples with high spatial resolution. The new probe design features smaller primary and spray channels and an entirely new configuration of the sampling port that enables robust imaging of tissues with a spatial resolution of 8-10 μm. We demonstrate the spatial resolution, sensitivity, durability, and throughput of the iMFP by imaging mouse uterine and brain tissue sections. The robustness of the high-resolution iMFP allowed us to perform first imaging experiments with both high spatial resolution and high throughput, which is particularly advantageous for high-resolution imaging of large tissue sections of interest to most MSI applications. Overall, the new probe design opens opportunities for mapping of biomolecules in biological samples with high throughput and cellular resolution, which is important for understanding biological systems.

Demonstration of an AI-driven workflow for autonomous high-resolution scanning microscopy

Modern scanning microscopes can image materials with up to sub-atomic spatial and sub-picosecond time resolutions, but these capabilities come with large volumes of data, which can be difficult to store and analyze. We report the Fast Autonomous Scanning Toolkit (FAST) that addresses this challenge by combining a neural network, route optimization, and efficient hardware controls to enable a self-driving experiment that actively identifies and measures a sparse but representative data subset in lieu of the full dataset. FAST requires no prior information about the sample, is computationally efficient, and uses generic hardware controls with minimal experiment-specific wrapping. We test FAST in simulations and a dark-field X-ray microscopy experiment of a WSe2 film. Our studies show that a FAST scan of <25% is sufficient to accurately image and analyze the sample. FAST is easy to adapt for any scanning microscope; its broad adoption will empower general multi-level studies of materials evolution with respect to time, temperature, or other parameters.

High-Throughput Mass Spectrometry Imaging of Biological Systems: Current Approaches and Future Directions

In the past two decades, the power of mass spectrometry imaging (MSI) for the label free spatial mapping of molecules in biological systems has been substantially enhanced by the development of approaches for imaging with high spatial resolution. With the increase in the spatial resolution, the experimental throughput has become a limiting factor for imaging of large samples with high spatial resolution and 3D imaging of tissues. Several experimental and computational approaches have been recently developed to enhance the throughput of MSI. In this critical review, we provide a succinct summary of the current approaches used to improve the throughput of MSI experiments. These approaches are focused on speeding up sampling, reducing the mass spectrometer acquisition time, and reducing the number of sampling locations. We discuss the rate-determining steps for different MSI methods and future directions in the development of high-throughput MSI techniques.

Mass Spectrometry Imaging for Single-Cell or Subcellular Lipidomics: A Review of Recent Advancements and Future Development

Mass Spectrometry Imaging (MSI) has emerged as a powerful imaging technique for the analysis of biological samples, providing valuable insights into the spatial distribution and structural characterization of lipids. The advancements in high-resolution MSI have made it an indispensable tool for single-cell or subcellular lipidomics. By preserving both intracellular and intercellular information, MSI enables a comprehensive analysis of lipidomics in individual cells and organelles. This enables researchers to delve deeper into the diversity of lipids within cells and to understand the role of lipids in shaping cell behavior. In this review, we aim to provide a comprehensive overview of recent advancements and future prospects of MSI for cellular/subcellular lipidomics. By keeping abreast of the cutting-edge studies in this field, we will continue to push the boundaries of the understanding of lipid metabolism and the impact of lipids on cellular behavior.

Deep Learning Approach for Dynamic Sampling for Multichannel Mass Spectrometry Imaging

Mass Spectrometry Imaging (MSI), using traditional rectilinear scanning, takes hours to days for high spatial resolution acquisitions. Given that most pixels within a sample's field of view are often neither relevant to underlying biological structures nor chemically informative, MSI presents as a prime candidate for integration with sparse and dynamic sampling algorithms. During a scan, stochastic models determine which locations probabilistically contain information critical to the generation of low-error reconstructions. Decreasing the number of required physical measurements thereby minimizes overall acquisition times. A Deep Learning Approach for Dynamic Sampling (DLADS), utilizing a Convolutional Neural Network (CNN) and encapsulating molecular mass intensity distributions within a third dimension, demonstrates a simulated 70% throughput improvement for Nanospray Desorption Electrospray Ionization (nano-DESI) MSI tissues. Evaluations are conducted between DLADS, a Supervised Learning Approach for Dynamic Sampling, with Least-Squares regression (SLADS-LS), and a Multi-Layer Perceptron (MLP) network (SLADS-Net). When compared with SLADS-LS, limited to a single m / z channel, as well as multichannel SLADS-LS and SLADS-Net, DLADS respectively improves regression performance by 36.7%, 7.0%, and 6.2%, resulting in gains to reconstruction quality of 6.0%, 2.1%, and 3.4% for acquisition of targeted m / z .

Emerging Computational Methods in Mass Spectrometry Imaging

Mass spectrometry imaging (MSI) is a powerful analytical technique that generates maps of hundreds of molecules in biological samples with high sensitivity and molecular specificity. Advanced MSI platforms with capability of high-spatial resolution and high-throughput acquisition generate vast amount of data, which necessitates the development of computational tools for MSI data analysis. In addition, computation-driven MSI experiments have recently emerged as enabling technologies for further improving the MSI capabilities with little or no hardware modification. This review provides a critical summary of computational methods and resources developed for MSI data analysis and interpretation along with computational approaches for improving throughput and molecular coverage in MSI experiments. This review is focused on the recently developed artificial intelligence methods and provides an outlook for a future paradigm shift in MSI with transformative computational methods.